

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Dict        =StableDiffusionPanoramaPipeline
			__A  :	Tuple        =TEXT_TO_IMAGE_PARAMS
			__A  :	Dict        =TEXT_TO_IMAGE_BATCH_PARAMS
			__A  :	Union[str, Any]        =TEXT_TO_IMAGE_IMAGE_PARAMS
			__A  :	List[Any]        =TEXT_TO_IMAGE_IMAGE_PARAMS






			def 		UpperCamelCase__							(  self     ):
				torch.manual_seed(0     )
				UpperCAmelCase_     :      Any						=  UNetaDConditionModel(
				    block_out_channels=(32, 64)			,layers_per_block=1			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,cross_attention_dim=32			,)
				UpperCAmelCase_     :      Union[str, Any]						=  DDIMScheduler()
				torch.manual_seed(0     )
				UpperCAmelCase_     :      List[Any]						=  AutoencoderKL(
				    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,latent_channels=4			,)
				torch.manual_seed(0     )
				UpperCAmelCase_     :      List[Any]						=  CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,intermediate_size=37			,layer_norm_eps=1E-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=10_00			,)
				UpperCAmelCase_     :      Union[str, Any]						=  CLIPTextModel(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

				UpperCAmelCase_     :      Dict						=  {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=0     ):
				UpperCAmelCase_     :      int						=  torch.manual_seed(_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  {
				    "prompt": "a photo of the dolomites",
				    "generator": generator,
				    # Setting height and width to None to prevent OOMs on CPU.
				    "height": None,
				    "width": None,
				    "num_inference_steps": 1,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_     :      Dict						=  self.get_dummy_components()
				UpperCAmelCase_     :      Optional[Any]						=  StableDiffusionPanoramaPipeline(**_snake_case     )
				UpperCAmelCase_     :      Dict						=  sd_pipe.to(_snake_case     )
				sd_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      List[str]						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      Any						=  sd_pipe(**_snake_case     ).images
				UpperCAmelCase_     :      Any						=  image[0, -3:, -3:, -1]
				assert image.shape == (1, 64, 64, 3)

				UpperCAmelCase_     :      List[Any]						=  np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				super().test_inference_batch_consistent(batch_sizes=[1, 2]     )






			def 		UpperCamelCase__							(  self     ):
				super().test_inference_batch_single_identical(batch_size=2			,expected_max_diff=3.25E-3     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_     :      Union[str, Any]						=  self.get_dummy_components()
				UpperCAmelCase_     :      Tuple						=  StableDiffusionPanoramaPipeline(**_snake_case     )
				UpperCAmelCase_     :      int						=  sd_pipe.to(_snake_case     )
				sd_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  "french fries"
				UpperCAmelCase_     :      Tuple						=  sd_pipe(**_snake_case			,negative_prompt=_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  output.images
				UpperCAmelCase_     :      Union[str, Any]						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				UpperCAmelCase_     :      Dict						=  np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_     :      Any						=  self.get_dummy_components()
				UpperCAmelCase_     :      Optional[Any]						=  StableDiffusionPanoramaPipeline(**_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  sd_pipe.to(_snake_case     )
				sd_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      Tuple						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      Dict						=  sd_pipe(**_snake_case			,view_batch_size=2     )
				UpperCAmelCase_     :      Optional[int]						=  output.images
				UpperCAmelCase_     :      List[Any]						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				UpperCAmelCase_     :      Any						=  np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[Any]						=  "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_     :      Optional[int]						=  self.get_dummy_components()
				UpperCAmelCase_     :      List[Any]						=  EulerAncestralDiscreteScheduler(
				    beta_start=0.00085			,beta_end=0.012			,beta_schedule="scaled_linear"     )
				UpperCAmelCase_     :      str						=  StableDiffusionPanoramaPipeline(**_snake_case     )
				UpperCAmelCase_     :      Dict						=  sd_pipe.to(_snake_case     )
				sd_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      Optional[Any]						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  sd_pipe(**_snake_case     ).images
				UpperCAmelCase_     :      Dict						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				UpperCAmelCase_     :      Union[str, Any]						=  np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  "cpu"  # ensure determinism for the device-dependent torch.Generator
				UpperCAmelCase_     :      List[Any]						=  self.get_dummy_components()
				UpperCAmelCase_     :      Tuple						=  PNDMScheduler(
				    beta_start=0.00085			,beta_end=0.012			,beta_schedule="scaled_linear"			,skip_prk_steps=_snake_case     )
				UpperCAmelCase_     :      Tuple						=  StableDiffusionPanoramaPipeline(**_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  sd_pipe.to(_snake_case     )
				sd_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      str						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      Optional[int]						=  sd_pipe(**_snake_case     ).images
				UpperCAmelCase_     :      List[Any]						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				UpperCAmelCase_     :      Optional[Any]						=  np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2




@slow
@require_torch_gpu
class 					_snake_case		(unittest.TestCase):






			def 		UpperCamelCase__							(  self     ):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 		UpperCamelCase__							(  self			,_snake_case=0     ):
				UpperCAmelCase_     :      Any						=  torch.manual_seed(_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  {
				    "prompt": "a photo of the dolomites",
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  "stabilityai/stable-diffusion-2-base"
				UpperCAmelCase_     :      int						=  DDIMScheduler.from_pretrained(_snake_case			,subfolder="scheduler"     )
				UpperCAmelCase_     :      Optional[Any]						=  StableDiffusionPanoramaPipeline.from_pretrained(_snake_case			,scheduler=_snake_case			,safety_checker=_snake_case     )
				pipe.to(_snake_case     )
				pipe.set_progress_bar_config(disable=_snake_case     )
				pipe.enable_attention_slicing()

				UpperCAmelCase_     :      Any						=  self.get_inputs()
				UpperCAmelCase_     :      List[str]						=  pipe(**_snake_case     ).images
				UpperCAmelCase_     :      Optional[Any]						=  image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 20_48, 3)

				UpperCAmelCase_     :      Optional[int]						=  np.array(
				    [
				        0.36968392,
				        0.27025372,
				        0.32446766,
				        0.28379387,
				        0.36363274,
				        0.30733347,
				        0.27100027,
				        0.27054125,
				        0.25536096,
				    ]     )

				assert np.abs(expected_slice - image_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  StableDiffusionPanoramaPipeline.from_pretrained(
				    "stabilityai/stable-diffusion-2-base"			,safety_checker=_snake_case     )
				UpperCAmelCase_     :      List[str]						=  LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
				pipe.to(_snake_case     )
				pipe.set_progress_bar_config(disable=_snake_case     )
				pipe.enable_attention_slicing()

				UpperCAmelCase_     :      str						=  self.get_inputs()
				UpperCAmelCase_     :      Optional[Any]						=  pipe(**_snake_case     ).images
				UpperCAmelCase_     :      int						=  image[0, -3:, -3:, -1].flatten()

				assert image.shape == (1, 5_12, 20_48, 3)

				UpperCAmelCase_     :      str						=  np.array(
				    [
				        [
				            0.0,
				            0.0,
				            0.0,
				            0.0,
				            0.0,
				            0.0,
				            0.0,
				            0.0,
				            0.0,
				        ]
				    ]     )

				assert np.abs(expected_slice - image_slice     ).max() < 1E-3






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[Any]						=  0

				def callback_fn(_snake_case			,_snake_case			,_snake_case     ) -> None:
					UpperCAmelCase_     :      Tuple						=  True
					nonlocal number_of_steps
					number_of_steps += 1
					if step == 1:
						UpperCAmelCase_     :      List[str]						=  latents.detach().cpu().numpy()
						assert latents.shape == (1, 4, 64, 2_56)
						UpperCAmelCase_     :      Tuple						=  latents[0, -3:, -3:, -1]

						UpperCAmelCase_     :      Optional[int]						=  np.array(
						    [
						        0.18681869,
						        0.33907816,
						        0.5361276,
						        0.14432865,
						        -0.02856611,
						        -0.73941123,
						        0.23397987,
						        0.47322682,
						        -0.37823164,
						    ]     )
						assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
					elif step == 2:
						UpperCAmelCase_     :      List[Any]						=  latents.detach().cpu().numpy()
						assert latents.shape == (1, 4, 64, 2_56)
						UpperCAmelCase_     :      Optional[int]						=  latents[0, -3:, -3:, -1]

						UpperCAmelCase_     :      Tuple						=  np.array(
						    [
						        0.18539645,
						        0.33987248,
						        0.5378559,
						        0.14437142,
						        -0.02455261,
						        -0.7338317,
						        0.23990755,
						        0.47356272,
						        -0.3786505,
						    ]     )

						assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2

				UpperCAmelCase_     :      int						=  False

				UpperCAmelCase_     :      List[Any]						=  "stabilityai/stable-diffusion-2-base"
				UpperCAmelCase_     :      Optional[Any]						=  DDIMScheduler.from_pretrained(_snake_case			,subfolder="scheduler"     )
				UpperCAmelCase_     :      Any						=  StableDiffusionPanoramaPipeline.from_pretrained(_snake_case			,scheduler=_snake_case			,safety_checker=_snake_case     )
				UpperCAmelCase_     :      int						=  pipe.to(_snake_case     )
				pipe.set_progress_bar_config(disable=_snake_case     )
				pipe.enable_attention_slicing()

				UpperCAmelCase_     :      Optional[int]						=  self.get_inputs()
				pipe(**_snake_case			,callback=_snake_case			,callback_steps=1     )
				assert callback_fn.has_been_called
				assert number_of_steps == 3






			def 		UpperCamelCase__							(  self     ):
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				UpperCAmelCase_     :      Dict						=  "stabilityai/stable-diffusion-2-base"
				UpperCAmelCase_     :      Union[str, Any]						=  DDIMScheduler.from_pretrained(_snake_case			,subfolder="scheduler"     )
				UpperCAmelCase_     :      Dict						=  StableDiffusionPanoramaPipeline.from_pretrained(_snake_case			,scheduler=_snake_case			,safety_checker=_snake_case     )
				UpperCAmelCase_     :      Tuple						=  pipe.to(_snake_case     )
				pipe.set_progress_bar_config(disable=_snake_case     )
				pipe.enable_attention_slicing(1     )
				pipe.enable_sequential_cpu_offload()

				UpperCAmelCase_     :      Union[str, Any]						=  self.get_inputs()
				UpperCAmelCase_     :      Dict						=  pipe(**_snake_case     )

				UpperCAmelCase_     :      Tuple						=  torch.cuda.max_memory_allocated()
				# make sure that less than 5.2 GB is allocated
				assert mem_bytes < 5.5 * 10**9





"""simple docstring"""



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__:List[str]									=			direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE__:Optional[int]									=			re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE__:Optional[Any]									=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__:int									=			[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def 	_lowerCamelCase(	a				):
       __a       =    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,    a				)
       return [m.group(0				) for m in matches]



def 	_lowerCamelCase(	):
       __a       =    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __a       =    {
           config.replace("Config"   ,    ""				): model_type for model_type, config in config_maping_names.items()
       }

       # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)

       # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
       for attr_name in dir(a				):
              __a       =    None
              if _re_tf_models.match(a				) is not None:
                     __a       =    tf_models
                     __a       =    _re_tf_models.match(a				).groups()[0]
              elif _re_flax_models.match(a				) is not None:
                     __a       =    flax_models
                     __a       =    _re_flax_models.match(a				).groups()[0]
              elif _re_pt_models.match(a				) is not None:
                     __a       =    pt_models
                     __a       =    _re_pt_models.match(a				).groups()[0]

              if lookup_dict is not None:
                     while len(a				) > 0:
                            if attr_name in model_prefix_to_model_type:
                                   __a       =    True
                                   break
                            # Try again after removing the last word in the name
                            __a       =    "".join(camel_case_split(a				)[:-1]				)

       __a       =    set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
       __a       =    list(a				)
       all_models.sort()

       __a       =    {"model_type": all_models}
       __a       =    [pt_models[t] for t in all_models]
       __a       =    [tf_models[t] for t in all_models]
       __a       =    [flax_models[t] for t in all_models]

       # Now let's use the auto-mapping names to make sure
       __a       =    {}
       for t in all_models:
              if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                     __a       =    "AutoProcessor"
              elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                     __a       =    "AutoTokenizer"
              elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                     __a       =    "AutoFeatureExtractor"
              else:
                     # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                     __a       =    "AutoTokenizer"

       __a       =    [processors[t] for t in all_models]

       return pd.DataFrame(a				)



def 	_lowerCamelCase(	a				):
       __a       =    [
           transformers_module.models.auto.modeling_auto,
           transformers_module.models.auto.modeling_tf_auto,
           transformers_module.models.auto.modeling_flax_auto,
       ]
       for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
              __a       =    [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
              __a       =    [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
              # Loop through all three frameworks
              for module, cls, mapping in zip(a   ,    a   ,    a				):
                     # The type of pipeline may not exist in this framework
                     if not hasattr(a   ,    a				):
                            continue
                     # First extract all model_names
                     __a       =    []
                     for name in getattr(a   ,    a				).values():
                            if isinstance(a   ,    a				):
                                   model_names.append(a				)
                            else:
                                   model_names.extend(list(a				)				)

            # Add pipeline tag and auto model class for those models
                     table.update({model_name: (pipeline_tag, cls) for model_name in model_names}				)

       return table



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    get_frameworks_table()
       __a       =    Dataset.from_pandas(a				)

       __a       =    hf_hub_download(
           "huggingface/transformers-metadata"   ,    "pipeline_tags.json"   ,    repo_type="dataset"   ,    token=a				)
       __a       =    Dataset.from_json(a				)
       __a       =    {
           tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
           for i in range(len(a				)				)
       }
       __a       =    update_pipeline_and_auto_class_table(a				)

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       __a       =    sorted(table.keys()				)
       __a       =    pd.DataFrame(
           {
               "model_class": model_classes,
               "pipeline_tag": [table[m][0] for m in model_classes],
               "auto_class": [table[m][1] for m in model_classes],
           }				)
       __a       =    Dataset.from_pandas(a				)

       with tempfile.TemporaryDirectory() as tmp_dir:
              frameworks_dataset.to_json(os.path.join(a   ,    "frameworks.json"				)				)
              tags_dataset.to_json(os.path.join(a   ,    "pipeline_tags.json"				)				)

              if commit_sha is not None:
                     __a       =    (
                         F"Update with commit {commit_sha}\n\nSee: "
                         F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     __a       =    "Update"

              upload_folder(
                  repo_id="huggingface/transformers-metadata"   ,    folder_path=a   ,    repo_type="dataset"   ,    token=a   ,    commit_message=a   ,    )



def 	_lowerCamelCase(	):
       __a       =    {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
       __a       =    transformers_module.pipelines.SUPPORTED_TASKS
       __a       =    []
       for key in pipeline_tasks:
              if key not in in_table:
                     __a       =    pipeline_tasks[key]["pt"]
                     if isinstance(a   ,    (list, tuple)				):
                            __a       =    model[0]
                     __a       =    model.__name__
                     if model not in in_table.values():
                            missing.append(a				)

       if len(a				) > 0:
              __a       =    ", ".join(a				)
              raise ValueError(
                  "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
                  F"`utils/update_metadata.py`: {msg}. Please add them!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]									=			argparse.ArgumentParser()
   parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
   parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
   parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)
